The method for QSAR modelling of rat acute toxicity based on the combination of QNA (Quantitative Neighbourhoods of Atoms) descriptors, PASS (Prediction of Activity Spectra for Substances) predictions and self-consistent regression (SCR) is presented. PASS predicted biological activity profiles are used as independent input variables for QSAR modelling with SCR. QSAR models were developed using LD50 values for compounds tested on rats with four types of administration (oral, intravenous, intraperitoneal, subcutaneous). The proposed method was evaluated on the set of compounds tested for acute rat toxicity with oral administration (7286 compounds) used for testing the known QSAR methods in T.E.S.T. 3.0 program (U.S. EPA). The several other sets of compounds tested for acute rat toxicity by different routes of administration selected from SYMYX MDL Toxicity Database were used too. The method was compared with the results of prediction of acute rodent toxicity for noncongeneric sets obtained by ACD/Labs Inc. The test sets were predicted with regards to the applicability domain. Comparison of accuracy for QSAR models obtained separately using QNA descriptors, PASS predictions, nearest neighbours’ assessment with consensus models clearly demonstrated the benefits of consensus prediction. Free available web-service for prediction of LD50 values of rat acute toxicity was developed: http://www.pharmaexpert.ru/GUSAR/AcuToxPredict/
Molecular Informatics, 2011, 30 (2-3), 241–250.

In the existing QSAR methods any molecule is represented as a single point in many-dimensional space of molecular descriptors. We proposed a new QSAR approach based on Quantitative Neighbourhoods of Atoms (QNA) descriptors, which characterize each atom of a molecule and depend on the whole molecule structure. In “Star Track” methodology any molecule is represented as a set of points in two-dimensional space of QNA descriptors. By our new method the estimate of target property of chemical compound is calculated as the average value of the function of QNA descriptors in the points of the atoms of a molecule in QNA descriptors’ space. Substantially, we have proposed to use only two instead of more than three thousand molecular descriptors applying in QSAR. On the basis of this approach we have developed computer program GUSAR and compared it with the several widely used QSAR methods including CoMFA, CoMSIA, Golpe/GRID, HQSAR and others, using ten data sets representing various chemical series and diverse types of biological activity. It was shown that in the majority of cases the accuracy and predictivity of GUSAR models appeared to be better than for the reference QSAR methods. High predictive ability and robustness of GUSAR were also shown in leave-20%-out cross-validation procedure.
SAR and QSAR in Environmental Research, 2009, 20 (7-8), 679-709.